Campaign optimization for experience content dataset

ABSTRACT

A server for campaign optimization is described. An experience content dataset is generated for an augmented reality application of a device based on analytics results. The analytics results are generated based on analytics data received from the device. The experience content dataset is provided to the device. The device recognizes a content identifier of the experience content dataset and generates an interactive experience with a presentation of virtual object content that is associated with the content identifier.

TECHNICAL FIELD

The subject matter disclosed herein generally relates to the processingof data. Specifically, the present disclosure addresses systems andmethods for a campaign optimization for experience content dataset.

BACKGROUND

A device can be used to generate additional data based on an imagecaptured with the device. For example, augmented reality (AR) is a live,direct or indirect, view of a physical, real-world environment whoseelements are augmented by computer-generated sensory input such assound, video, graphics or GPS data. With the help of advanced ARtechnology (e.g. adding computer vision and object recognition) theinformation about the surrounding real world of the user becomesinteractive. Artificial information about the environment and itsobjects can be overlaid on the real world.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings.

FIG. 1 is a block diagram illustrating an example of a network suitablefor operating a campaign optimizer, according to some exampleembodiments.

FIG. 2 is a block diagram illustrating modules (e.g., components) of aserver, according to some example embodiments.

FIG. 3 is a block diagram illustrating modules (e.g., components) of acampaign optimizer, according to some example embodiments.

FIG. 4 is a block diagram illustrating an example of an operation of thecampaign optimizer, according to some example embodiments.

FIG. 5 is a block diagram illustrating an example of an operation of theanalytics computation, according to some example embodiments.

FIG. 6 is a block diagram illustrating modules (e.g., components) of adevice, according to some example embodiments.

FIG. 7 is a block diagram illustrating modules (e.g., components) of acontextual local image recognition module, according to some exampleembodiments.

FIG. 8 is a block diagram illustrating modules (e.g., components) of theanalytics tracking module, according to some example embodiments

FIG. 9 is a schematic diagram illustrating an example of generating andutilization of an optimization campaign, according to some exampleembodiments.

FIG. 10 is a flowchart illustrating an example method for optimizing acampaign, according to some example embodiments.

FIG. 11 is a flowchart illustrating another example method foroptimizing a campaign, according to some example embodiments.

FIG. 12 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium and perform any one or more of the methodologiesdiscussed herein.

DETAILED DESCRIPTION

Example methods and systems are directed to a campaign optimization forexperience content dataset. Examples merely typify possible variations.Unless explicitly stated otherwise, components and functions areoptional and may be combined or subdivided, and operations may vary insequence or be combined or subdivided. In the following description, forpurposes of explanation, numerous specific details are set forth toprovide a thorough understanding of example embodiments. It will beevident to one skilled in the art, however, that the present subjectmatter may be practiced without these specific details.

A server for campaign optimization is described. An experience contentdataset is generated for an augmented reality application of a devicebased on analytics results. The analytics results are generated based onanalytics data received from the device. The experience content datasetis provided to the device. The device recognizes a content identifier ofthe experience content dataset and generates an interactive experiencewith a presentation of virtual object content that is associated withthe content identifier.

Augmented reality applications allow a user to experience additionalinformation, such as in the form of a three-dimensional virtual objectoverlaid on a picture of a physical object captured by a camera of adevice. The physical object may include a visual reference (alsoreferred to as a content identifier) that the augmented realityapplication can identify and recognize. A visualization of theadditional information, such as the three-dimensional virtual objectengaged with an image of the physical object is generated in a displayof the device. The three-dimensional virtual object may be based on therecognized visual reference. A rendering of the visualization of thethree-dimensional virtual object may be based on a position of thedisplay relative to the visual reference.

The server may access analytics results for the device. In oneembodiment, the server builds a new experience content dataset for theexperience content dataset based on the analytics results for thedevice. In another embodiment, the server modifies an originalexperience content dataset into the experience content dataset based onthe analytics results for the device.

The server may generate a virtual object model using the experiencecontent dataset. The virtual object model may be rendered in a displayof the device based on a position of the device relative to a physicalobject recognized as the content identifier. A presentation of thevirtual object may be based on a real-time image of the physical objectcaptured with the device. The virtual object model may be associatedwith an image of the physical object.

In one embodiment, the server may receive pose estimation data of thedevice relative to the physical object captured with the device, poseduration data of the device relative to the physical object capturedwith the device, pose orientation data of the device relative to thephysical object captured with the device, and pose interaction data ofthe device relative to the physical object captured with the device.Pose estimation data may include a location on the physical or virtualobject aimed by the device. Pose duration data may include a timeduration within which the device is aimed at a same location on thephysical or virtual object. Pose orientation data may include anorientation of the device aimed at the physical or virtual object. Poseinteraction data may include interactions of the user on the device withrespect the virtual object corresponding to the physical object.

In one embodiment, the content identifier may include a two-dimensionalimage or a three-dimensional object model. The virtual object contentmay include a two-dimensional or three-dimensional virtual object model.The experience generator may associate the content identifier with thevirtual object content to generate the experience content dataset.

In one embodiment, the two-dimensional or three-dimensional virtualobject model has at least one interactive feature that changes a stateof the two-dimensional or three-dimensional virtual object model inresponse to an interaction from a user on the device. The server maychange an interactive feature of the virtual object content from theexperience content dataset based on the analytics results.

In one embodiment, the analytics data may include usage conditions ofthe device, the usage conditions of the device comprising socialinformation of a user of the device, location usage information, andtime information of the device using the augmented reality application.

In another embodiment, the server may also generate analytics resultsbased on analytics data received from the device. The analytics resultsmay be provided to the campaign optimization module to generate anenhanced experience content dataset based on the analytics results. Forexample, the experience at the device may be customized based on theuser device usage history, which picture and which part of the picturethe user used the device to point at, a length time corresponding to thepart of the picture the device was pointed at, and so forth.

FIG. 1 is a network diagram illustrating a network environment 100suitable for operating an augmented reality application of a device,according to some example embodiments. The network environment 100includes a device 101, a client 112, and a server 110, communicativelycoupled to each other via a network 108. The device 101, the client 112,and the server 110 may each be implemented in a computer system, inwhole or in part, as described below with respect to FIG. 12.

The server 110 may be part of a network-based system. For example, thenetwork-based system may be or includes a cloud-based server system thatprovides campaign optimization for an experience content dataset. Theclient 112 may access the campaign optimization module in the server 110via a web-browser or a programmatic client to target specific audiencesor users.

A user 102 may use the device 101 to experience an interactive contentgenerated by the experience content dataset generated by the server 110.In another example, the user 102 may use the client 112 to use thecontent creation tool of the server 110 to generate the interactivecontent on the device 101. The user may be a human user (e.g., a humanbeing), a machine user (e.g., a computer configured by a softwareprogram to interact with the device 101), or any suitable combinationthereof (e.g., a human assisted by a machine or a machine supervised bya human). The user 102 is not part of the network environment 100, butis associated with the device 101 and may be a user of the device 101.For example, the device 101 may be a desktop computer, a vehiclecomputer, a tablet computer, a navigational device, a portable mediadevice, or a smart phone belonging to the user 102.

The user 102 may be a user of an application in the device 101. Theapplication may include an augmented reality application configured toprovide the user 102 with an experience triggered with a physical objectsuch as, a two-dimensional physical object 104 (e.g., a picture) or athree-dimensional physical object 106 (e.g., a car). For example, theuser 102 may point a lens of the device 101 to capture an image of thetwo-dimensional physical object 104. The image is recognized locally inthe device 101 using a local context recognition dataset module of theaugmented reality application of the device 101. The augmented realityapplication then generates additional information (e.g., an interactivethree-dimensional model) in a display of the device 101 in response toidentifying the recognized image. If the capture image is not recognizedlocally at the device 101, the device 101 downloads thethree-dimensional model corresponding to the captured image, from adatabase of the server 110 over the network 108.

The device 101 may capture and submit analytics data to the server 110for further analysis on usage and how the user 102 is engaged with thephysical object. For example, the analytics data may include where inparticular on the physical or virtual object the user 102 has looked at,how long the user 102 has looked at each location on the physical orvirtual object, how the user 102 held the device 101 when looking at thephysical or virtual object, which features of the virtual object theuser 102 interacted with (e.g., such as whether a user tapped on a linkin the virtual object). The analytics data may be processed at theserver 110 to generate an enhanced content dataset or modified contentdataset based on an analysis of the analytics data. The device 101 mayreceive and generate a virtual object with additional or enhancedfeatures or a new experience based on the enhanced content dataset.

Any of the machines, databases, or devices shown in FIG. 1 may beimplemented in a general-purpose computer modified (e.g., configured orprogrammed) by software to be a special-purpose computer to perform oneor more of the functions described herein for that machine, database, ordevice. For example, a computer system able to implement any one or moreof the methodologies described herein is discussed below with respect toFIG. 12. As used herein, a “database” is a data storage resource and maystore data structured as a text file, a table, a spreadsheet, arelational database (e.g., an object-relational database), a triplestore, a hierarchical data store, or any suitable combination thereof.Moreover, any two or more of the machines, databases, or devicesillustrated in FIG. 1 may be combined into a single machine, and thefunctions described herein for any single machine, database, or devicemay be subdivided among multiple machines, databases, or devices.

The network 108 may be any network that enables communication between oramong machines (e.g., server 110), databases, and devices (e.g., device101). Accordingly, the network 108 may be a wired network, a wirelessnetwork (e.g., a mobile or cellular network), or any suitablecombination thereof. The network 108 may include one or more portionsthat constitute a private network, a public network (e.g., theInternet), or any suitable combination thereof.

FIG. 2 is a block diagram illustrating modules (e.g., components) of aserver, according to some example embodiments. The server 110 includes acampaign optimization module 202, an experience generator 204, ananalytics computation module 206, and a database 208 in a storagedevice.

The campaign optimization module 202 may generate an experience contentdataset for an augmented reality application of the device 101 based onanalytics results (from the device or other devices). The campaignoptimization module 202 is described in more details below with respectto FIG. 3.

The experience generator 204 may provide the experience content datasetto the device 101 that recognizes the content identifier, and generatean interactive experience with the virtual object content at the device101. In one embodiment, the experience generator 204 generate a virtualobject model using the experience content dataset to be rendered in adisplay of the device 101 based on a position of the device 101 relativeto a physical object such as the two-dimensional physical object 104(e.g., a picture) or the three-dimensional physical object 106 (e.g., acar). The device 101 recognizes the two-dimensional physical object 104(e.g., a picture) or the three-dimensional physical object 106 (e.g., acar) as a content identifier. The visualization of the virtual objectmay correspond to the virtual object model engaged with a real-timeimage of the physical object captured with the device 101. The virtualobject model may be based on an image of the physical object.

The analytics computation module 206 may operate on analytics datareceived from the device or other devices to generate analytics results,and to provide the analytics results to the campaign optimization module202 so that the campaign optimization module 202 can generate a new ormodified experience content dataset based on the analytics results. Forexample, an additional animation or feature may be provided andassociated with a location most often viewed by the user. In anotherexample, personalized information may be provided in a presentation ofthe virtual content (virtual billboard) with scores or statistics of theuser's favorite teams.

In one embodiment, the analytics computation module 206 analyzes a poseestimation of the device 101 relative to the physical object capturedwith the device 101, a pose duration of the device 101 relative to thephysical object captured with the device 101, a pose orientation of thedevice relative to the physical object captured with the device 101, anda pose interaction of the device relative to the physical objectcaptured with the device 101. The pose estimation may include a locationon the physical or virtual object aimed by the device. The pose durationmay include a time duration within which the device is aimed at a samelocation on the physical or virtual object. The pose orientation mayinclude an orientation of the device aimed at the physical or virtualobject. The pose interaction may include interactions of the user on thedevice with respect the virtual object corresponding to the physicalobject.

The database 208 may include experience content dataset 212, andanalytics and results data 214.

The experience content dataset 212 may include datasets generated basedon content creation template data using a content creation tool. Forexample, the datasets may include a table of interactive virtualcontents and corresponding physical contents.

The analytics and results data 214 may include analytics data receivedfrom devices. For example, the analytics data may include poseestimation data, pose duration data, pose orientation data, poseinteraction data, sentiment data, among others. The analytics andresults data 214 may include results data from an analysis of theanalytics data with the analytics computation module 206. Results datamay include most often used features or most often looked at location ofa virtual content generated from one of the experience content dataset212.

FIG. 3 is a block diagram illustrating modules (e.g., components) of thecampaign optimization module 202, according to some example embodiments.The campaign optimization module 202 includes an analytics retriever302, an experience content dataset builder 306, and an experiencecontent dataset modifier 304.

In one embodiment, the analytics retriever 302 accesses analyticsresults for the device 101. The experience content dataset builder 306builds a new experience content dataset for the experience contentdataset based on the analytics results for the device 101. Theexperience content dataset modifier 304 modifies an original experiencecontent dataset into the experience content dataset based on theanalytics results for the device 101.

In another embodiment, the analytics retriever 302 accesses analyticsresults and analytics data from devices having previously generatedinteractive experiences with the content identifier. The experiencecontent dataset builder 306 may build a new experience content datasetfor the experience content dataset based on the analytics results andanalytics data for the devices having previously generated interactiveexperiences with the content identifier. The experience content datasetmodifier 304 may modify an original experience content dataset into theexperience content dataset based on the analytics results and analyticsdata for the devices having previously generated interactive experienceswith the content identifier.

In one embodiment, the content identifier includes a two-dimensionalimage or a three-dimensional object model. The virtual object contentmay include two-dimensional or three-dimensional virtual object model.The experience generator 204 may associate the content identifier withthe virtual object content to generate the experience content dataset.

FIG. 4 is a block diagram illustrating an example of an operation of thecampaign optimization module 202, according to some example embodiments.The campaign optimization module 202 receives analytics data 402 andanalytics results processed by the analytics computation module 206.

The experience content dataset builder 306 builds an optimizedexperience content dataset 408 as a new experience content dataset basedon the analytics data 402 or analytics results 404 for the device 101 orfor other devices. For example, the experience content database builder306 accesses a three-dimensional virtual content (e.g., athree-dimensional virtual car with animated features) and atwo-dimensional virtual content (e.g., a picture) selected based on theanalytics data 402 and analytics data 404. The experience contentdataset builder 306 associates the physical content model with thevirtual content module to generate the optimized experience contentdataset 408. The optimized experience content dataset 408 can becommunicated to the device 101 so that when the device 101 recognizes ascanned picture from the optimized experience content dataset 408, anexperience corresponding to the recognized scanned picture is generatedat the device 101. The experience may include enabling the user 102 tointeract with specific interactive features or layout of the virtualobject pertinent to the user of the device 101. The specific interactivefeatures or layout of the virtual object is presented in combinationwith a real-time representation of the scanned picture in the display ofthe device 101.

The experience content dataset modifier 304 modifies an existingoriginal experience content dataset 406 into the optimized experiencecontent dataset based on the analytics data 402 or the analytics results404 for the device 101 or for other devices. For example, the experiencecontent database modifier 304 accesses a three-dimensional virtualcontent (e.g., a three-dimensional virtual car with animated features)and a two-dimensional virtual content (e.g., a picture) of the existingoriginal experience content dataset 406. The experience content databasemodifier 304 modifies a feature or a presentation of thethree-dimensional virtual content based on the analytics data 402 andanalytics data 404. The experience content dataset modifier 306associates the physical content model with the modified virtual contentmodule to generate the optimized experience content dataset 408. Theoptimized experience content dataset 408 can be communicated to thedevice 101 so that when the device 101 recognizes a scanned picture fromthe optimized experience content dataset 408, an experiencecorresponding to the recognized scanned picture is generated at thedevice 101. The experience may include enabling the user 102 to interactwith modified interactive features or layout of the virtual objectpertinent to the user of the device 101. The modified interactivefeatures or layout of the virtual object are presented in combinationwith a real-time representation of the scanned picture in the display ofthe device 101.

FIG. 5 is a block diagram illustrating an example of an operation of theanalytics computation module 206, according to some example embodiments.The analytics computation module 206 operates on analytics data 402. Inone embodiment, analytics data 402 include pose estimation data 502,pose duration data 508, pose orientation data 506, and pose interactiondata 508.

Pose estimation data 502 may include the location on a virtual object orphysical object the device 101 is aiming at. For example, the device 101may aim at the top of a virtual statue generated by aiming the device101 at the physical object 104. In another example, the device 101 mayaim at the shoes of a person in a picture of a magazine.

Pose duration data 504 may include a time duration within which thedevice 101 is aimed at a same location on the physical or virtualobject. For example, pose duration data 504 may include the length ofthe time the user 102 has aimed and maintained the device at the shoesof a person in the magazine. User sentiment and interest of the shoesmay be inferred based on the length of the time the user 102 has heldthe device 101 aimed at the shoes.

Pose orientation data 506 may be configured to determine an orientationof the device aimed at the physical or virtual object. For example, thepose orientation module 506 may determine that the user 102 is holdingthe device 101 in a landscape mode and thus may infer a sentiment orinterest based on the orientation of the device 101.

Pose interaction data 508 may include data on interactions of the user102 on the device 101 with respect the virtual object corresponding tothe physical object. For example, the virtual object may includefeatures such as virtual menus or button. When the user 102 taps on thevirtual button, a browser application in the device 101 is launched to apreselected website associated with the tapped virtual dialog box. Poseinteraction data 508 may include data measuring and determining whichbutton the user 102 has tapped on, how often the user 102 has tapped onwhich button, the click through rate for each virtual buttons, websitesvisited by the user 102 from an augmented application, and so forth.

The analytics computation module 206 analyzes the data submitted todetermine patterns, trends using statistical algorithms. For example,the analytics computation module 206 may determine features most used orclicked on, colors of virtual object clicked on the most or least, areasof the virtual object viewed the most, and so forth. The resultingcomputation of the analytics computation module 206 may be referred toas analytics results 404.

FIG. 6 is a block diagram illustrating modules (e.g., components) of thedevice 101, according to some example embodiments. The device 101 mayinclude sensors 602, a display 604, a processor 606, and a storagedevice 616. For example, the device 101 may be a desktop computer, avehicle computer, a tablet computer, a navigational device, a portablemedia device, or a smart phone of a user. The user may be a human user(e.g., a human being), a machine user (e.g., a computer configured by asoftware program to interact with the device 101), or any suitablecombination thereof (e.g., a human assisted by a machine or a machinesupervised by a human).

The sensors 602 may include, for example, a proximity sensor, an opticalsensor (e.g., charged-coupled device (CCD)), an orientation sensor(e.g., gyroscope), an audio sensor (e.g., a microphone). For example,the sensors 602 may include a rear facing camera and a front facingcamera in the device 101. It is noted that the sensors described hereinare for illustration purposes and the sensors 602 are thus not limitedto the ones described.

The display 604 may include, for example, a touchscreen displayconfigured to receive a user input via a contact on the touchscreendisplay. In another example, the display 604 may include a screen ormonitor configured to display images generated by the processor 606.

The processor 606 may include a contextual local image recognitionmodule 608, a consuming application such as an augmented realityapplication 609, and an analytics tracking module 618.

The augmented reality application 609 may generate a visualization of athree-dimensional virtual object overlaid on an image of a physicalobject captured by a camera of the device 101 in the display 604 of thedevice 101. A visualization of the three-dimensional virtual object maybe manipulated by adjusting a position of the physical object relativeto the camera of the device 101. Similarly, the visualization of thethree-dimensional virtual object may be manipulated by adjusting aposition of the device 100 relative to the physical object.

In one embodiment, the augmented reality application 609 communicateswith the contextual local image recognition dataset module 608 in thedevice 101 to retrieve three-dimensional models of virtual objectsassociated with a captured image. For example, the captured image mayinclude a visual reference (also referred to as a marker) that consistsof an identifiable image, symbol, letter, number, machine-readable code.For example, the visual reference may include a bar code, a QR code, oran image that has been previously associated with a three-dimensionalvirtual object.

In another embodiment, the augmented reality application 609 may allow auser to select an experience from a virtual menu. The experience mayinclude different virtual object content.

The contextual local image recognition dataset module 608 may beconfigured to determine whether the captured image matches an imagelocally stored in a local database of images and correspondingadditional information (e.g., three-dimensional model and interactivefeatures) on the device 101. In one embodiment, the contextual localimage recognition module 608 retrieves a primary content dataset fromthe server 110, generates and updates a contextual content dataset basedan image captured with the device 101.

The analytics tracking module 618 may track analytics data related tohow the user 102 is engaged with the physical object. For example, theanalytics tracking module 618 may track where on the physical or virtualobject the user 102 has looked at, how long the user 102 has looked ateach location on the physical or virtual object, how the user 102 heldthe device 101 when looking at the physical or virtual object, whichfeatures of the virtual object the user 102 interacted with (e.g., suchas whether a user tapped on a link in the virtual object).

The storage device 616 may be configured to store a database of visualreferences (e.g., images) and corresponding experiences (e.g.,three-dimensional virtual objects, interactive features of thethree-dimensional virtual objects). For example, the visual referencemay include a machine-readable code or a previously identified image(e.g., a picture of shoe). The previously identified image of the shoemay correspond to a three-dimensional virtual model of the shoe that canbe viewed from different angles by manipulating the position of thedevice 101 relative to the picture of the shoe. Features of thethree-dimensional virtual shoe may include selectable icons on thethree-dimensional virtual model of the shoe. An icon may be selected oractivated by tapping or moving on the device 101.

In one embodiment, the storage device 604 includes a primary contentdataset 610, a contextual content dataset 612, an enhanced contentdataset 622, and analytics data 620.

The primary content dataset 610 includes, for example, a first set ofimages and corresponding experiences (e.g., interactivethree-dimensional virtual object models). The primary content dataset610 may include a core set of images or the most popular imagesdetermined by the server 110. The core set of images may include alimited number of images identified by the server 110. For example, thecore set of images may include the cover image of the ten most popularmagazines and the corresponding experiences (e.g., virtual objects). Inanother example, the server 110 may generate the first set of imagesbased on the most popular or often scanned images received at the server110.

The contextual content dataset 612 includes, for example, a second setof images and corresponding experiences (e.g., three-dimensional virtualobject models) retrieved from the server 110. For example, imagescaptured with the device 101 that are not recognized in the primarycontent dataset 610 are submitted to the server 110 for recognition. Ifthe captured image is recognized by the server, a correspondingexperience may be downloaded at the device 101 and stored in thecontextual content dataset 612.

The analytics data 620 corresponds to analytics data collected by theanalytics tracking module 618.

The enhanced content dataset 622 includes, for example, an enhanced setof images and corresponding experiences downloaded from the server 110based on the analytics data collected by the analytics tracking module618. In one embodiment, the enhanced content dataset 622 may include theoptimized experience content dataset 408.

In one embodiment, the device 101 may communicate over the network 108with the server 110 to retrieve a portion of a database of visualreferences, corresponding three-dimensional virtual objects, andcorresponding interactive features of the three-dimensional virtualobjects. The network 108 may be any network that enables communicationbetween or among machines, databases, and devices (e.g., the device101). Accordingly, the network 108 may be a wired network, a wirelessnetwork (e.g., a mobile or cellular network), or any suitablecombination thereof. The network may include one or more portions thatconstitute a private network, a public network (e.g., the Internet), orany suitable combination thereof.

Any one or more of the modules described herein may be implemented usinghardware (e.g., a processor of a machine) or a combination of hardwareand software. For example, any module described herein may configure aprocessor to perform the operations described herein for that module.Moreover, any two or more of these modules may be combined into a singlemodule, and the functions described herein for a single module may besubdivided among multiple modules. Furthermore, according to variousexample embodiments, modules described herein as being implementedwithin a single machine, database, or device may be distributed acrossmultiple machines, databases, or devices.

FIG. 7 is a block diagram illustrating modules (e.g., components) of acontextual local image recognition dataset module 608, according to someexample embodiments. The contextual local image recognition datasetmodule 608 may include an image capture module 702, a local imagerecognition module 704, a content request module 706, and a contextcontent dataset update module 708.

The image capture module 702 may capture an image with a lens of thedevice 101. For example, the image capture module 702 may capture theimage of a physical object pointed at by the device 101. In oneembodiment, the image capture module 702 may capture one image or aseries of snapshots. In another embodiment, the image capture module 702may capture an image when sensors 602 (e.g., vibration, gyroscope,compass, etc.) detect that the device 101 is no longer moving.

The local image recognition module 704 determines that the capturedimage correspond to an image stored in the primary content dataset 610and locally renders the three-dimensional virtual object modelcorresponding to the image captured with the device 101 when the imagecaptured with the device 101 correspond to one of the set of images ofthe primary content dataset 610 stored in the device 101.

In another embodiment, the local image recognition module 704 determinesthat the captured image correspond to an image stored in the contextcontent dataset 612 and locally renders the three-dimensional virtualobject model corresponding to the image captured with the device 101when the image captured with the device 101 corresponds to one of theset of images of the context content dataset 612 stored in the device101.

The content request module 706 may request the server 110 for thethree-dimensional virtual object model corresponding to the imagecaptured with the device 101 when the image captured with the device 101does not correspond to one of the set of images in the primary contentdataset 612 and the context content dataset 612 in the storage device604.

The context content dataset update module 708 may receive thethree-dimensional virtual object model corresponding to the imagecaptured with the device 101 from the server 110 in response to therequest generated by the content request module 706. In one embodiment,the context content dataset update module 708 may update the contextualcontent dataset 612 with the three-dimensional virtual object modelcorresponding to the image captured with the device 101 from the server110 when the image captured with the device 101 does not correspond toany images stored locally in the storage device 604 of the device 101.

In another embodiment, the content request module 706 may determineusage conditions of the device 101 and generate a request to the server110 for a third set of images and corresponding three-dimensionalvirtual object models based on the usage conditions. The usageconditions may be related to when, how often, where, and how the user isusing the device 101. The context content dataset update module 708 mayupdate the contextual content dataset with the third set of images andcorresponding three-dimensional virtual object models.

For example, the content request module 706 determines that the user 102scans pages of a newspaper in the morning time. The content requestmodule 706 then generates a request to the server 110 for a set ofimages and corresponding experiences that are relevant to usage of theuser 102 in the morning. For example, the content request module 706 mayretrieve images of sports articles that the user 102 is most likely toscan in the morning and a corresponding updated virtual score board ofthe team mentioned in the article. The experience may include, forexample, a fantasy league score board update personalized to the user102.

In another example, the content request module 706 determines that theuser 102 often scans the business section of a newspaper. The contentrequest module 706 then generates a request to the server 110 for a setof images and corresponding experiences that are relevant to the user102. For example, the content request module 706 may retrieve images ofbusiness articles of the next issue of the newspaper as soon as the nextissue business articles are available. The experience may include, forexample, a video report corresponding to an image of the next issuebusiness article.

In yet another embodiment, the content request module 706 may determinesocial information of the user 102 of the device 101 and generate arequest to the server 110 for another set of images and correspondingthree-dimensional virtual object models based on the social information.The social information may be obtained from a social network applicationin the device 101. The social information may relate to who the user 102has interacted with, who the user 102 has shared experiences using theaugmented reality application 609 of the device 101. The context contentdataset update module 708 may update the contextual content dataset withthe other set of images and corresponding three-dimensional virtualobject models.

For example, the user 102 may have scanned several pages of a magazine.The content request module 706 determines from a social networkapplication that the user 102 is friend with another user who sharesimilar interests and read another magazine. As such, the contentrequest module 706 may generate a request to the server 110 for a set ofimages and corresponding experiences related to the other magazine.

In another example, if the content request module 706 determines thatthe user 102 has scanned one or two images from the same magazine, thecontent request module 706 may generate a request for additional contentfrom other images in the same magazine.

FIG. 8 is a block diagram illustrating modules (e.g., components) of theanalytics tracking module 618, according to some example embodiments.The analytics tracking module 618 includes a pose estimation module 802,a pose duration module 804, a pose orientation module 806, and a poseinteraction module 808.

The pose estimation module 802 may be configured to detect the locationon a virtual object or physical object the device 101 is aiming at. Forexample, the device 101 may aim at the top of a virtual statue generatedby aiming the device 101 at the physical object 104. In another example,the device 101 may aim at the shoes of a person in a picture of amagazine.

The pose duration module 804 may be configured to determine a timeduration within which the device 101 is aimed at a same location on thephysical or virtual object. For example, the pose duration module 804may measure the length of the time the user 102 has aimed and maintainedthe device at the shoes of a person in the magazine. Sentiment andinterest of the shoes may be inferred based on the length of the timethe user 102 has held the device 101 aimed at the shoes.

The pose orientation module 806 may be configured to determine anorientation of the device aimed at the physical or virtual object. Forexample, the pose orientation module 806 may determine that the user 102is holding the device 101 in a landscape mode and thus may infer asentiment or interest based on the orientation of the device 101.

The pose interaction module 808 may be configured to determineinteractions of the user 102 on the device 101 with respect the virtualobject corresponding to the physical object. For example, the virtualobject may include features such as virtual menus or button. When theuser 102 taps on the virtual button, a browser application in the device101 is launched to a preselected website associated with the tappedvirtual dialog box. The pose interaction module 408 may measure anddetermine which buttons the user 102 has tapped on, the click throughrate for each virtual buttons, websites visited by the user 102 from theaugmented reality application 609, and so forth.

FIG. 9 is a schematic diagram illustrating an example of consuming anexperience, according to some example embodiments. The device 901 may bepointed at a physical object 904 having an image 906 that is recognizedby the device 901. The device 901 submits a hash of the image 916 alongwith analytics data 922 of the device 901 to the server 110. The server110 also receives data analytics 924, 926, and 928 from other devices930, 932, and 934 having previously pointed to the same image 906. Inanother embodiment, data analytics 924, 926, and 928 include data fromdevices 930, 932, and 934 having previously pointed to the other imagesor objects.

The campaign optimization module 202 at the server 110 generates anoptimized experience content dataset 918 customized for the device 901.The device 901 generates a representation of a virtual object 908 in adisplay 902 of the device 901. The optimized experience 920 may include,for example, the virtual object 908 with personalized points of interest910, 912, and 914 particularly relevant to the user of the device 901.For example, the optimized experience content dataset 918 might includea virtual object or a sound favored by the user.

In another embodiment, the campaign optimization module 202 at theserver 110 generates an optimized experience content dataset 918 basedon the aggregate analytics data 916, 924, 926, and 928. The device 901generates a representation of the virtual object 908 in the display 902of the device 901. The optimized experience 920 may include, forexample, the virtual object 908 with the most popular points of interest910, 912, and 914 as determined from an analysis of the aggregateanalytics data 924, 926, and 928.

In another embodiment, the optimized experience 920 may include, forexample, most looked at interactive features with points of interests912, 910, 914 of the three-dimensional virtual object 908. In oneembodiment, a rendering engine at the device renders thethree-dimensional virtual object 908.

FIG. 10 is a flowchart illustrating an example method for optimizing acampaign, according to some example embodiments.

At operation 1002, a campaign optimization module of a server accessesanalytics data and results from device(s).

At operation 1004, the campaign optimization module modifies an originalexperience content dataset for device(s) based on analytics data andresults from the device(s).

At operation 1006, the campaign optimization module sends the modifiedexperience content dataset to the device(s).

FIG. 11 is a flowchart illustrating another example method foroptimizing a campaign, according to some example embodiments.

At operation 1102, a campaign optimization module of a server accessesanalytics data and results from device(s).

At operation 1104, the campaign optimization module builds a newexperience content dataset for device(s) based on analytics data andresults from the device(s).

At operation 1106, the campaign optimization module sends the modifiedexperience content dataset to the device(s).

FIG. 12 is a block diagram illustrating components of a machine 1200,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium, acomputer-readable storage medium, or any suitable combination thereof)and perform any one or more of the methodologies discussed herein, inwhole or in part. Specifically, FIG. 12 shows a diagrammaticrepresentation of the machine 1200 in the example form of a computersystem and within which instructions 1224 (e.g., software, a program, anapplication, an applet, an app, or other executable code) for causingthe machine 1200 to perform any one or more of the methodologiesdiscussed herein may be executed, in whole or in part. In alternativeembodiments, the machine 1200 operates as a standalone device or may beconnected (e.g., networked) to other machines. In a networkeddeployment, the machine 1200 may operate in the capacity of a servermachine or a client machine in a server-client network environment, oras a peer machine in a distributed (e.g., peer-to-peer) networkenvironment. The machine 1200 may be a server computer, a clientcomputer, a personal computer (PC), a tablet computer, a laptopcomputer, a netbook, a set-top box (STB), a personal digital assistant(PDA), a cellular telephone, a smartphone, a web appliance, a networkrouter, a network switch, a network bridge, or any machine capable ofexecuting the instructions 1224, sequentially or otherwise, that specifyactions to be taken by that machine. Further, while only a singlemachine is illustrated, the term “machine” shall also be taken toinclude a collection of machines that individually or jointly executethe instructions 1224 to perform all or part of any one or more of themethodologies discussed herein.

The machine 1200 includes a processor 1202 (e.g., a central processingunit (CPU), a graphics processing unit (GPU), a digital signal processor(DSP), an application specific integrated circuit (ASIC), aradio-frequency integrated circuit (RFIC), or any suitable combinationthereof), a main memory 1204, and a static memory 1206, which areconfigured to communicate with each other via a bus 1208. The machine1200 may further include a graphics display 1210 (e.g., a plasma displaypanel (PDP), a light emitting diode (LED) display, a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)). The machine1200 may also include an alphanumeric input device 1212 (e.g., akeyboard), a cursor control device 1214 (e.g., a mouse, a touchpad, atrackball, a joystick, a motion sensor, or other pointing instrument), astorage unit 1216, a signal generation device 1218 (e.g., a speaker),and a network interface device 1220.

The storage unit 1216 includes a machine-readable medium 1222 on whichis stored the instructions 1224 embodying any one or more of themethodologies or functions described herein. The instructions 1224 mayalso reside, completely or at least partially, within the main memory1204, within the processor 1202 (e.g., within the processor's cachememory), or both, during execution thereof by the machine 1200.Accordingly, the main memory 1204 and the processor 1202 may beconsidered as machine-readable media. The instructions 1224 may betransmitted or received over a network 1226 (e.g., network 108) via thenetwork interface device 1220.

As used herein, the term “memory” refers to a machine-readable mediumable to store data temporarily or permanently and may be taken toinclude, but not be limited to, random-access memory (RAM), read-onlymemory (ROM), buffer memory, flash memory, and cache memory. While themachine-readable medium 1222 is shown in an example embodiment to be asingle medium, the term “machine-readable medium” should be taken toinclude a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storeinstructions. The term “machine-readable medium” shall also be taken toinclude any medium, or combination of multiple media, that is capable ofstoring instructions for execution by a machine (e.g., machine 1200),such that the instructions, when executed by one or more processors ofthe machine (e.g., processor 1202), cause the machine to perform any oneor more of the methodologies described herein. Accordingly, a“machine-readable medium” refers to a single storage apparatus ordevice, as well as “cloud-based” storage systems or storage networksthat include multiple storage apparatus or devices. The term“machine-readable medium” shall accordingly be taken to include, but notbe limited to, one or more data repositories in the form of asolid-state memory, an optical medium, a magnetic medium, or anysuitable combination thereof.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied on a machine-readable medium or ina transmission signal) or hardware modules. A “hardware module” is atangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware modules of a computer system (e.g., a processor or a groupof processors) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

In some embodiments, a hardware module may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware module may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware module may be a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an ASIC. A hardware module may alsoinclude programmable logic or circuitry that is temporarily configuredby software to perform certain operations. For example, a hardwaremodule may include software encompassed within a general-purposeprocessor or other programmable processor. It will be appreciated thatthe decision to implement a hardware module mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software) may be driven by cost and timeconsiderations.

Accordingly, the phrase “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. As used herein,“hardware-implemented module” refers to a hardware module. Consideringembodiments in which hardware modules are temporarily configured (e.g.,programmed), each of the hardware modules need not be configured orinstantiated at any one instance in time. For example, where a hardwaremodule comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware modules) at different times. Software mayaccordingly configure a processor, for example, to constitute aparticular hardware module at one instance of time and to constitute adifferent hardware module at a different instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multiplehardware modules exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware modules. In embodiments inwhich multiple hardware modules are configured or instantiated atdifferent times, communications between such hardware modules may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware modules have access.For example, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented module” refers to ahardware module implemented using one or more processors.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, a processor being an example of hardware. Forexample, at least some of the operations of a method may be performed byone or more processors or processor-implemented modules. Moreover, theone or more processors may also operate to support performance of therelevant operations in a “cloud computing” environment or as a “softwareas a service” (SaaS). For example, at least some of the operations maybe performed by a group of computers (as examples of machines includingprocessors), with these operations being accessible via a network (e.g.,the Internet) and via one or more appropriate interfaces (e.g., anapplication program interface (API)).

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Some portions of the subject matter discussed herein may be presented interms of algorithms or symbolic representations of operations on datastored as bits or binary digital signals within a machine memory (e.g.,a computer memory). Such algorithms or symbolic representations areexamples of techniques used by those of ordinary skill in the dataprocessing arts to convey the substance of their work to others skilledin the art. As used herein, an “algorithm” is a self-consistent sequenceof operations or similar processing leading to a desired result. In thiscontext, algorithms and operations involve physical manipulation ofphysical quantities. Typically, but not necessarily, such quantities maytake the form of electrical, magnetic, or optical signals capable ofbeing stored, accessed, transferred, combined, compared, or otherwisemanipulated by a machine. It is convenient at times, principally forreasons of common usage, to refer to such signals using words such as“data,” “content,” “bits,” “values,” “elements,” “symbols,”“characters,” “terms,” “numbers,” “numerals,” or the like. These words,however, are merely convenient labels and are to be associated withappropriate physical quantities.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or any suitable combination thereof), registers, orother machine components that receive, store, transmit, or displayinformation. Furthermore, unless specifically stated otherwise, theterms “a” or “an” are herein used, as is common in patent documents, toinclude one or more than one instance. Finally, as used herein, theconjunction “or” refers to a non-exclusive “or,” unless specificallystated otherwise.

What is claimed is:
 1. A server comprising: one or more hardwareprocessors of the server comprising a campaign optimization module, ananalytics computation module, and an experience generator, the campaignoptimization module configured to generate a second experience contentdataset for an augmented reality application of a device based onanalytics results of other users interactions on other devices thatinclude a first experience content dataset, the second experiencecontent dataset comprising a second virtual object and a second set ofuser interactive features of the second virtual object; the analyticscomputation module configured to generate the analytics results based onanalytics data received from the other devices, the analytics data basedon the other users interactions with a first virtual object displayed onthe other devices and a first set of user interactive features of thefirst virtual object from the first experience dataset; the experiencegenerator configured to provide the second experience content dataset tothe device, the device configured to recognize a physical contentidentifier from the second experience content dataset, to identify thephysical content identifier in an image captured with a camera of thedevice, and to display in the device the second virtual object and thesecond set of user interactive features of the second virtual object inresponse to identifying the physical content identifier in the image,and a storage device configured to store the augmented realityapplication and the first and second experience content datasets.
 2. Theserver of claim 1, wherein the campaign optimization module comprises:an analytics retriever configured to access analytics results from thedevice; an experience content dataset builder configured to build athird experience content dataset to replace the first experience contentdataset based on the analytics results from the device and from theother devices; and an experience content dataset modifier configured tomodify the first experience content dataset based on the analyticsresults from the device and from the other devices.
 3. The server ofclaim 1, wherein the campaign optimization module comprises: ananalytics retriever configured to access analytics results and analyticsdata from the other devices, the other devices having previouslydisplayed the first virtual object and the first set of user interactivefeatures of the first virtual object from the first experience dataset;an experience content dataset builder configured to build the secondexperience content dataset to replace the first experience contentdataset based on the analytics results and analytics data from the otherdevices; and an experience content dataset modifier configured to modifythe first experience content dataset based on the analytics results andanalytics data from the other devices.
 4. The server of claim 1, whereinthe experience generator is configured to generate a first virtualobject model using the first experience content dataset, the firstvirtual object model to be rendered in a display of the device based ona position of the device relative to a physical object recognized as thephysical content identifier, and a presentation of the first virtualobject being based on a real-time image of the physical object capturedwith the device, the first virtual object model associated with an imageof the physical object.
 5. The server of claim 1, wherein the analyticscomputation module is configured to receive pose estimation data of thedevice relative to the physical object captured with the device, poseduration data of the device relative to the physical object capturedwith the device, pose orientation data of the device relative to thephysical object captured with the device, and pose interaction data ofthe device relative to the physical object captured with the device,wherein the pose estimation data comprises a location on the physical orfirst virtual object aimed by the device, wherein the pose duration datacomprises a time duration within which the device is aimed at a samelocation on the physical or first virtual object, wherein the poseorientation data comprises an orientation of the device aimed at thephysical or first virtual object, and wherein the pose interaction datacomprises interactions of the user on the device with the first virtualobject corresponding to the physical object.
 6. The server of claim 1,wherein the physical content identifier identifies a two-dimensionalphysical object or a three-dimensional physical object, wherein thefirst virtual object comprises a two-dimensional or three-dimensionalvirtual object model, and wherein the experience generator is configuredto associate the physical content identifier with the first virtualobject to generate the first experience content dataset.
 7. The serverof claim 6, wherein the two-dimensional or three-dimensional virtualobject model has at least one user interactive feature, the at least oneuser interactive feature changing a state of the two-dimensional orthree-dimensional virtual object model in response to an interactionfrom a user on the device.
 8. The server of claim 6, wherein thecampaign optimization module is configured to change a user interactivefeature of the first virtual object from the first experience contentdataset based on the analytics results of the other user interactions onthe other devices.
 9. The server of claim 1, wherein the analytics datacomprises usage conditions of the device.
 10. The server of claim 9,wherein the usage conditions of the device comprises social informationof a user of the device, location usage information, and timeinformation of the device using the augmented reality application.
 11. Acomputer-implemented method comprising: generating a second experiencecontent dataset for an augmented reality application of a device basedon analytics results of other users interactions on other devices thatinclude a first experience content dataset, the second experiencecontent dataset comprising a second virtual object and a second set ofuser interactive features of the second virtual object; generating,using at least one hardware processor of a server, the analytics resultsbased on analytics data received from the other devices, the analyticsdata based on the other users interactions with a first virtual objectdisplayed on the other devices and a first set of user interactivefeatures of the first virtual object from the first experience dataset;providing the second experience content dataset to the device, thedevice configured to recognize a physical content identifier from thesecond experience content dataset, to identify the physical contentidentifier in an image captured with a camera of the device, and todisplay in the device the second virtual object and the second set ofuser interactive features of the second virtual object in response toidentifying the physical content identifier in the image.
 12. Thecomputer-implemented method of claim 11, further comprising: accessingthe analytics results from the device; building a third experiencecontent dataset to replace the first experience content dataset based onthe analytics results from the device and from the other devices; andmodifying the first experience content dataset based on the analyticsresults from the device and from the other devices.
 13. Thecomputer-implemented method of claim 11, further comprising: accessinganalytics results and analytics data from other devices, the otherdevices having previously displayed the first virtual object and thefirst set of user interactive features of the first virtual object fromthe first experience content dataset; building the second experiencecontent dataset to replace the first experience content dataset based onthe analytics results and analytics data from the other devices; andmodifying the first experience content dataset based on the analyticsresults and analytics data from the other devices.
 14. Thecomputer-implemented method of claim 11, further comprising: generatinga first virtual object model using the first experience content dataset,the first virtual object model to be rendered in a display of the devicebased on a position of the device relative to a physical objectrecognized as the physical content identifier, a presentation of thefirst virtual object being based on a real-time image of the physicalobject captured with the device, the first virtual object modelassociated with an image of the physical object.
 15. Thecomputer-implemented method of claim 11, further comprising: receivingpose estimation data of the device relative to the physical objectcaptured with the device, pose duration data of the device relative tothe physical object captured with the device, pose orientation data ofthe device relative to the physical object captured with the device, andpose interaction data of the device relative to the physical objectcaptured with the device, wherein the pose estimation data comprises alocation on the physical or first virtual object aimed by the device,wherein the pose duration data comprises a time duration within whichthe device is aimed at a same location on the physical or first virtualobject, wherein the pose orientation data comprises an orientation ofthe device aimed at the physical or first virtual object, and whereinthe pose interaction data comprises interactions of the user on thedevice with the first virtual object corresponding to the physicalobject.
 16. The computer-implemented method of claim 11, wherein thephysical content identifier identifies a two-dimensional physical objector a three-dimensional physical object, wherein the first virtual objectcomprises a two-dimensional or three-dimensional virtual object model,and further comprising: associating the physical content identifier withthe first virtual object to generate the first experience contentdataset.
 17. The computer-implemented method of claim 16, wherein thetwo-dimensional or three-dimensional virtual object model has at leastone user interactive feature, the at least one user interactive featurechanging a state of the two-dimensional or three-dimensional virtualobject model in response to an interaction from a user on the device.18. The computer-implemented method of claim 16, further comprising:changing a user interactive feature of the first virtual object from thefirst experience content dataset based on the analytics results of theother user interactions on the other devices.
 19. Thecomputer-implemented method of claim 11, wherein the analytics datacomprises usage conditions of the device, the usage conditions of thedevice comprising social information of a user of the device, locationusage information, and time information of the device using theaugmented reality application.
 20. A non-transitory machine-readablemedium comprising instructions that, when executed by one or moreprocessors of a machine, cause the machine to perform operationscomprising: generating a second experience content dataset for anaugmented reality application of a device based on analytics results ofusers interactions on other devices that include a first experiencecontent dataset, the second experience content dataset comprising asecond virtual object and a second set of user interactive features ofthe second virtual object; generating the analytics results based onanalytics data received from the other devices, the analytics data basedon the other users interactions with a first virtual object displayed onthe other devices and a first set of user interactive features of thefirst virtual object from the first experience dataset; providing thesecond experience content dataset to the device, the device configuredto recognize a physical content identifier from the second experiencecontent dataset, to identify the physical content identifier in an imagecaptured with a camera of the device, and to display in the device thesecond virtual object and the second set of user interactive features ofthe second virtual object in response to identifying the physicalcontent identifier in the image.